1. Field of the Invention
The present disclosure refers to a method for detecting geometrical structures in images, in particular in images of chemical and/or biological samples, such as images of cells. Specifically, the present disclosure refers to a method for detecting cell traces.
2. Discussion of the Background Art
Cell traces are understood as straight objects originating from the borders of cells. They vary in width and length and include varying angles with the cell surface. This phenomenon is described in detail in ZIMMERMANN, H., E. RICHTER, C. REICHLE, L WESTPHAL, P. GEGGIER, U. REHN, S. ROGASCHEWSKI, W. BLEISS and G. R. FUHR: Mammalian Cell Traces-Morphology, Molecular Composition, Artificial Guidance and Biotechnological Relevance as a New Type of “Bionanotube”, Appl. Phys. A., May 2001.
For a detection of linear objects in image analysis, the linear Hough transformation has prevailed. It is described in LEAVERS, V. F.: Which Hough Transform. CVGIP: Image Understanding, 58(2):250-264, 1993. Here, the image is transformed from a real space into a Hough space whose dimensions are defined by line parameters. The problem of line detection is thus transformed into a problem of filtering maximum values from a transformed. The method in turn is a special case of general Hough transformation as described, for example, in U.S. Pat. No. 3,069,654.
Starting from the original linear transformation, various improvements were proposed.
Generally, a global transformation of an image is not useful, since the lines to be detected often appear only in a part of the image; as soon as coarser structures (e.g. large surfaces) are present in other parts of the image, the lines can no longer be unambiguously identified from the transformed.
For example, US 2002/0012466 A1 describes one possibility of restricting the real space to be transformed. Herein, a user is frequently given the opportunity during the analysis to manually limit the region. The process is ended interactively, as soon as the quality of the transform meets with the demands of the user.
Despite a locally limited treatment of the real space, maxima may be produced in the transformed space that correspond to no object actually present in the real space. This phenomenon occurs, for example, if the intensities “incidentally” detected during the transformation of a point are higher than those actually associated with the object (“correlated noise”, Leavers, see above).
Murakami, in MURAKAMI, K. and T. NARUSE: High-Speed Line Detection Method Using Hough Transform in Local Area. Systems and Computers in Japan, 32(10): 918-926, 2001, proposes an approach for reducing this effect, wherein only a respective section of the real space is transformed; lines that extend beyond an observed section, however, have to be correlated with other sections in an additional step.
In The Dynamic Generalized Hough Transform. in: Curves and Surfaces in Computer Vision and Graphics, vol. 1251, p. 281-292, SPIE, August 1990, Leavers describes an approach to a solution, wherein the transformation is first performed only for one point; the Hough space forming has one dimension less than in the case of a complete transformation. Thus, for line detection, a one-dimensional space is given. Even at that moment, the same is examined for maxima. When a maximum is present, not only the corresponding coordinates are included, but the points belonging to the object are eliminated from the number of image points to be transformed. Thus, it is prevented that an element once recognized as an object point contributes to other portions of the Hough space. The method is repeated as long as the image set still contains elements. One problem of this analysis is the analysis at extreme locations in the transformed, since the one-dimensional parameter spaces are observed separately. A concrete threshold value for the maxima is hard to predict, the more so since the objects generally vary in size.
In SHPILMAN, IL and V. URAILOVSKY: Fast and Robust Techniques for Detecting Straight Line Segments Using Locals Models. Pattern Recognition Letters, 20:865-877, 1999, Sphilman et al. describe a method for detecting straight lines, wherein existing knowledge about partial points of the objects to be detected is used. Herein, one-dimensional parameter spaces are employed. The image to be analyzed is pre-processed by an edge filter, so that, from the outset, the algorithm includes only points lying on lines. For each of these points pk, a one-dimensional histogram is generated that indicates the angle under which as many of the remaining points qk form a line with p. This method produces errors, if the pre-processing leaves too many points that are not located on lines. Another drawback mentioned by the authors is that such points as are in the vicinity of p can not be included in the analysis; the transformation algorithm is based on the determination of the intersection of the line pqk and a fixed reference line and will lead to distortions should the distance of p and qk is too short. Moreover, there still is a possibility of erroneously detected lines (see FIG. 9(b) in Shpilman, above).
In the field of biology, Hough transformation was already employed by Lyazghi et al (LYAZGHI, A., C. DECAESTEKER, I. CAMBY, R. KISS and P. V. HAM: Characterisation of Actin Filaments in Cancer Cells by the Hough Transform. In: Signal Processing, Pattern Recognition and Applications, p. 138-142. IASTED, July 2001.) for detecting filaments within the cell skeleton of cancer cells. In this method, initially false maxima are also allowed in the (integral) transformation of the cell surface. The extreme locations are verified only after a post-treatment. Here, the authors compare the length of the corresponding line with the lengths to be expected, the cell diameter serving as the maximum dimension.